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Open AccessArticle

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

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USDA Forest Service Pacific Northwest Research Station, University of Washington, P.O. Box 352100, Seattle, WA 98195-2100, USA
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Washington State Department of Natural Resources, P.O. Box 47000, 1111 Washington Street SE, Olympia, WA 98504-7000, USA
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Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
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Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, Peavy 204, OR 97331-5706, USA
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Author to whom correspondence should be addressed.
Forests 2017, 8(11), 444; https://doi.org/10.3390/f8110444
Received: 29 September 2017 / Revised: 30 October 2017 / Accepted: 10 November 2017 / Published: 16 November 2017
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria. View Full-Text
Keywords: forest inventory; dbh; diameter distribution; performance criteria forest inventory; dbh; diameter distribution; performance criteria
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Strunk, J.L.; Gould, P.J.; Packalen, P.; Poudel, K.P.; Andersen, H.-E.; Temesgen, H. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. Forests 2017, 8, 444.

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